A SLAM (Simultaneous Localization and Mapping) process (e.g., algorithm) can be used by a mobile computing device (e.g., mobile phone, tablet, wearable augmented reality (AR) device, wearable, autonomous aerial or ground vehicle, or a robot) to map the structure of a physical environment surrounding the mobile computing device and to localize the mobile computing device's relative position within that mapped environment. A SLAM process can usually map and localize, in real-time, as the mobile computing device moves about within its physical environment.
Although not exclusively image-based, some SLAM processes achieve mapping and localization by using images of the physical environment provided by an image sensor associated with the mobile computing device, such as a mobile phone's built-in camera. From the captured images, such SLAM processes can recover the mobile computing device position and construct a map of the physical environment surrounding the mobile computing device, by recovering both the image sensor's pose and the structure of the map without initially knowing either.
SLAM processes that use captured images usually require several images of corresponding physical features (hereafter, features), in the physical environment, that are captured by an image sensor (e.g., of a mobile computing device) at different poses. The images captured from the different camera locations permit such SLAM processes to converge and start their localization and mapping processes. Unfortunately, the localization problem in an image-based SLAM process is usually difficult to solve due to errors in matching corresponding features between captured images—these errors tend to move the local result of the minimization problem of the SLAM process to a local minimum rather than a global minima, which provides a specific location.